Method and device for converting video signals

ABSTRACT

The invention relates to a method and a device for movement vector assisted conversion of a first video signal containing a first image sequence at a first frequency into a second video signal containing a second image sequence at a second frequency. At least some of the images in the second image sequence, which are phase shifted in relation to the first image sequence, are formed by a interpolation of images in the first image sequence in such a manner that, pixel points of at least one first image and a second image are filtered by means of a median filter, for a pixel of an image in the second image sequence median. The median filter is an adoptively weighted median filter.

[0001] The invention relates to a method and a device for converting video signals.

[0002] In order achieve the optimum display of video signals in an image, an image format which is adapted in terms of the temporal or spatial sampling raster to the display used to reproduce the image or toth-e basic multimedia environment is required. To this end, it is often necessary to perform a temporal and/or spatial conversion between different video formats. The temporal conversion here comprises in particular the generation of images with a video frequency that deviates from the video frequency of an original video signal to be converted. The spatial conversion may in particular include the generation of image information of the interlaced lines of a field to generate a frame.

[0003] In the development of interpolation methods for moving image sequences¹, special importance is attached to a fluid display of motion and the avoidance of interpolation artifacts or resolution losses.

PRIOR ART

[0004] Extensive prior art exists which provides methods and equipment for converting video signals, and the following discussion will explain this technology for better understanding of the invention. For reasons of readability, the description contains only bracketed references to the specific documents. A list of complete references for the documents cited is provided at the end of the description.

[0005] A multiplicity of algorithms exist that provide temporal and/or spatial conversion which may be classified as either motion-adaptive or motion-vector-based methods, although in each case either linear or nonlinear interpolation techniques may be employed.

[0006] The purpose of the following discussion is to offer a brief presentation of various existing methods for spatial/temporal format conversion. Static and motion-adaptive methods are explained first. Consideration is then given to motion-vector-based methods that form the basis for the method according to the invention.

Static and Motion-Adaptive Methods

[0007] A number of static or linear methods of format conversion exist which will be discussed only briefly due to the many disadvantage associated with these methods, such as loss of resolution or blurring of motion.

[0008] The simplest means of temporal format conversion consists of an image repetition which, however, produces an unsatisfactory reproduction of motion having multiple contours or “jerky” motion sequences due to the temporally incorrect rendition of motion. Linear temporal low-pass filtering results in blurring of motion, and is thus similarly ill-suited for the interpolation of moving regions.

[0009] Static linear methods exist which are based on vertical or temporal filtering and are used to achieve a spatial conversion of an interlaced signal, i.e., a signal by which sequential interlaced fields are transmitted, to a progressive video signal, i.e., a signal that contains frames—so-called proscan conversion.

[0010] Since vertical low-pass filtering results in loss of resolution in the vertical axis, while temporal low-pass filtering causes motion blurring in moving image regions, methods have been developed that adaptively cross-fade between temporal filtering in nonmoving image regions and vertical filtering in moving image regions. Methods of this type are explained in reference [9].

[0011] Due to the disadvantages of linear interpolation techniques, methods were developed that employ nonlinear median filters for interpolation. The basic design and the function of a median filter is described, for example, in reference [11]. Median filters for image processing are described, for example, in reference [12].

[0012] Median filtering sorts input values of the filter by size and selects the value located at the center of the sorted sequence, the sequence usually consisting of values of an odd number.

[0013] Reference [1] thus presents a method for proscan conversion based on a linear vertical band separation of the video signal into a highs channel and a lows channel, and on the use of complementary median filters in the highs channel and lows channel.

[0014] The principle on which use of a spatial band separation is based is the fact that human perception may be described by a two-channel model. In the model, there exists a lows channel with a low spatial but high temporal resolution, and a highs channel with a low temporal but high spatial resolution.

[0015] Reference [1] describes a spatial conversion method of this type using band separation in which a missing intermediate line is generated in a field, the method being explained based on FIG. 1. FIG. 1 shows three sequential fields A₁, B₁, A₂ after band separation into a lows channel and a highs channel, where two sequential fields A₁ and B₁, or B₁ and A₁, have mutually shifted image rasters. The lines for which the particular image information is transmitted are identified by boxes. According to the known method, the image information of a pixel (x, y), which lies in an intermediate line, is generated in a field B₁ of the lows channel, i.e., in the field at time T_(n) by median filtering from the adjacent pixels in the vertical axis (x, y−1) and (x, y+1) of the same field, and from the pixel at position (x, y) in the following field A₂, i.e., the field at time Tn+1. If P(.) represents the image information of the given pixel, then the following equations apply for the image information of the pixel (x, y) in the field B₁ of the lows channel, or: $\begin{matrix} {{P\left( {x,y,T_{n}} \right)} = {{Med}\begin{Bmatrix} {P\left( {x,{y - 1},T_{n}} \right)} \\ {P\left( {x,{y + 1},T_{n}} \right)} \\ {P\left( {x,y,T_{n + 1}} \right)} \end{Bmatrix}}} & (1) \end{matrix}$

[0016] where Med {.} stands for median filtering.

[0017] In analogous fashion, the image information of pixel (x, y) in the intermediate line of field B₁ in the highs channel is generated by: $\begin{matrix} {{P\left( {x,y,T_{n}} \right)} = {{Med}\begin{Bmatrix} {P\left( {x,{y - 1},T_{n}} \right)} \\ {P\left( {x,{y + 1},T_{n}} \right)} \\ {P\left( {x,y,T_{n + 1}} \right)} \end{Bmatrix}}} & (2) \end{matrix}$

[0018] Whereas for the interpolation of the image information of pixel (x, y) in the lows channel, image information of pixels from only two fields is processed, namely, from fields B₁ and A₂ in FIG. 1, image information of pixels from three fields, namely, from fields A₁, B₁ and A₂ is used for the interpolation of the image information of pixel (x, y) in the highs channel which essentially contains the image details.

[0019] The filtering for the lows channel is vertically (intrafield-dominant) dominant since two of the three pixels involved are oriented vertically above each other in the image. The filtering for the highs channel is temporally (interfield-dominant) or raster-dominant since the three pixels involved in filtering derive from three temporally successive fields.

[0020] The vertically dominant median filter in the lows channel enables very good rendition of motion, in horizontally moving vertical edges, for example. It results, however, in resolution losses in vertically high-frequency, i.e., rapidly changing, image regions which are of secondary importance, however, in the lows channel. The raster-dominant median filter used in the highs channel, on the other hand, has the high vertical resolution required in the highs channel, but results, however, in a poor rendition of motion in this channel. Additional methods based on modified or edge-direction-oriented median filters are presented in reference [1].

[0021] The method described with subband-based signal processing for spatial conversion, i.e., the generation of intermediate lines, was extended to a temporal up-conversion from 50 Hz interlaced signals to 100 Hz interlaced signals, i.e., to a conversion method in which fields with a 100 Hz image sequence are generated from fields with an image sequence of 50 Hz.

[0022] The resulting interpolation scheme of the lows channel for the interpolation of an intermediate field identified as β that lies temporally exactly between two original fields A₁ and B₁ is shown in FIG. 2 and is explained specifically in references [1] and [4]. This static interpolation scheme enables a positionally correct interpolation of moving edges or other large objects within a certain velocity range. The basis for this is the edge shift property of median filters which will be discussed in more detail below.

[0023] A 5-tap median filter is used in the highs channel for intermediate image interpolation. This is illustrated in FIG. 3. It is evident here that, unlike the lows channel, no pixels re-interpolated in the raster are supplied to the median filter so as to preclude any vertical resolution loss that is critical in the highs channel. Every other field of the input sequence is taken over directly here as the field for the output image sequence.

[0024] A more detailed survey of static and motion-adaptive methods for intermediate image interpolation is found in reference [9].

Motion-Vector-Based Methods

[0025] Even using the above-described error-tolerant interpolation concept, static interpolation methods only permit a correct display of motion in the intermediate image to be achieved up to a certain velocity range which is a function of the interpolation mask size. In addition, loss of resolution may occur for moving image information in the highs channel even with raster-dominant median filters.

[0026] For this reason, methods employing a motion-vector-based interpolation have been developed for the high-quality interpolation of moving image sequences. In this method, a motion vector (v_(x), v_(y)) is assigned to each pixel using an appropriate motion estimation method, the vector indicating by how many raster positions in the x-axis and y-axis a given pixel has moved from one image/field to the next image/field. Document [9], for example, provides a survey of appropriate motion estimation methods for assigning a motion vector to a pixel or group of pixels, and this document is referenced here.

[0027] By incorporating this type of motion vector in the interpolation, or by motion-vector-based addressing of the interpolation filters, as FIG. 4 illustrates schematically, a correct interpolation may be performed even in rapidly moving image regions, assuming an error-free estimation of motion and a purely translational motion.

[0028] With reference to FIG. 4, the basic concept of motion-vector-based interpolation is to determine a motion vector V_(AnBn) from positions of a moving pixel in successive images/fields A_(n)(T_(−10 ms)) and B_(n)(T_(−10 ms)), which vector indicates by how many raster points the pixel has moved, and to interpolate pixels which lie on the path of the motion vector between the positions in images/fields A_(n)(T⁻¹⁰ ms) and B_(n)(T_(−10 ms)) based on the image information about the moving pixel and motion vector in one or more intermediate images which lie temporally between the successive images/fields A_(n)(T_(−10 ms)) and B_(n)(T_(−10 ms)).

[0029] As is the case for static and motion-adaptive interpolation, both linear and nonlinear methods exist for motion-vector-based interpolation.

[0030] Assuming that the positions of the moving pixel, i.e., the starting point and end point of the motion vector in the images/fields A_(n)(T_(−10 ms)) and B_(n)(T_(−10 ms)), and thus the motion vector, are precisely known, it is sufficient in a simple linear method to perform a simple shift or a linear averaging. Reference [5] presents a subband-based interpolation method which performs averaging in the lows channel between the two pixels addressed by the starting point and end point of the motion vector—i.e., for the image information of a pixel (x, y) in an intermediate image β_(n), the following equation applies:

P _(β)(x, y, T ₀)=½[A _(n)(x−v _(x)/2, y−v _(y)/2, T _(−10ms))+B _(n)(x+v _(x)/2, y+V _(y)/2, T _(+10ms)),

[0031] where A_(n) (x−v_(x)/2, y−v_(y)/2, T_(+10ms)) denotes the image information of the pixel in the field sequence An at time T_(−10ms) which represents the starting point of the motion vector, and where B_(n) (x+v_(x)/2, y+V_(y)/2, T_(+10ms)) denotes the image information of the pixel in the field sequence B_(n) at time T_(+10ms) which represents the end point of the motion vector. The terms v_(x) and v_(y) are the components of the estimated motion vector V_(AnBn) for the pixel (x−v_(x)/2, y−v_(y)/2) in the field A_(n) (T−10ms).

[0032] In reference [5], the image information for the intermediate image is determined in the highs channel by a vector-based shift, in other words, for the image information of pixel (x, y) in an intermediate image β_(n), the applicable equation is:

P ₆₂ (x, y, T ₀)=A _(n) (x−v _(x)/2, y−v_(y)/2, T_(−10ms))

or

P _(β)(x, y, T ₀)=B _(n)(x+v _(x)/2, y+v _(y)/2, T ^(+10ms)).

[0033] This method has a poor error tolerance, however, in the case of faulty, i.e., incorrectly estimated motion vectors.

[0034] For this reason, reference [6] presents a nonlinear interpolation method based on a 3-tap median filter. Here, the image information for a pixel (x, y) of an intermediate image β interpolated according to FIG. 4 is determined by median filtering of image information for the starting point and end point of the motion vector, and of a mean of the image information from the starting point and end point as follows: $\begin{matrix} {{P_{\beta}\left( {x,y,T_{n}} \right)} = {{Med}\begin{Bmatrix} {A_{n}\left( {{x - {v_{x}/2}},{y - {v_{y}/2}},T_{{- 10}\quad m\quad s}} \right.} \\ {1/{2\left\lbrack {{A_{n}\left( {x,y,T_{{- 10}\quad m\quad s}} \right)} + {B_{n}\left( {x,y,T_{{+ 10}\quad m\quad s}} \right)}} \right.}} \\ {B_{n}\left( {{x + {v_{x}/2}},{y + {v_{y}/2}},T_{{+ 10}\quad m\quad s}} \right)} \end{Bmatrix}}} & (3) \end{matrix}$

[0035] In the case of a correctly estimated vector, the pixels selected in image A_(n) and image B_(n) based on the motion vector—the starting points and end points of the motion vector—are identical, and thus form the initial value of the median filter. In the case of faulty estimation of motion, i.e., when the starting points and end points of the motion vector do not contain the same image information, linear averaging results as a fall-back mode, along with the resulting blurring of motion.

[0036] The vector-based intermediate image interpolation methods described so far do not have sufficient tolerance for faulty estimations for the motion vector, i.e., for vector errors. An improvement in the image quality of interpolated images is possible by using weighted vector-based median filters as described, for example, in connection with the methods explained in references [1, 2, 3].

[0037] Here, as in the non-vector-based method described in reference [4], a spatial band separation is performed.

[0038] The interpolation scheme for the lows channel is shown in the illustration of FIG. 5. A median filter is supplied here with image information from multiple pixels which are located around the starting pixel of the motion vector V_(AnBn) in the field A_(n)(T_(−10ms)) and around the end pixel of the motion vector V_(AnBn) in the field B_(n)(T^(+10ms)). The median filter is also supplied with image information, in the form of so-called recursive elements, for already determined adjacent pixels of the intermediate image β_(n) For purposes of illustration, the median filter in FIG. 5 is supplied with image information from nine pixels of field A_(n) (T_(−10ms)), nine pixels of field B_(n)(T_(+10ms)), and three pixels of intermediate image β_(n) (T₀). The image information supplied to the median filter may be variously weighted, the weighting factor indicating how often the image information of a pixel is supplied to the median filter.

[0039] The pixels of the lows channel to be interpolated are calculated as follows: $\begin{matrix} {{P_{\beta}\left( {x,y,T_{\beta}} \right)} = {{Med}\begin{Bmatrix} W_{An} & {P_{An}\left( {{x - {v_{x}/2}},{y - {v_{y}/2}},T_{An}} \right)} \\ W_{Bn} & {P_{Bn}\left( {{x + {v_{x}/2}},{y + {v_{y}/2}},T_{Bn}} \right)} \\ W_{\beta \quad n} & {P_{\beta \quad n}\left( {{x - 1},y,T_{\beta \quad n}} \right)} \end{Bmatrix}}} & (4) \end{matrix}$

[0040] W_(An), W_(Bn) and W_(βn) describe masks around the specific vector-addressed pixels and denotes the duplication operator which indicates how often a sampling value is introduced into the filter mask. The pixels in the fields A_(n)(T_(−10ms)) and Bn (T_(+10ms)), around which filter masks are positioned, are each shifted relative to the pixel (x, y) to be interpolated in the intermediate image β_(n) (T₀) by a fraction of the motion vector, this fraction in FIG. 5 corresponding to half the motion vector since the intermediate image β_(n) (T₀) to be interpolated is located temporally precisely in the center between the fields A_(n)(T_(−10ms)) and B_(n)(T_(+10ms)).

[0041] If the motion vector in one of the fields indicates an image line which does not exist in the corresponding field, a re-interpolation takes place, as described in reference [9].

[0042] The advantage of vector-based weighted median filters is the fact that they are able to correct faulty motion vectors up to a certain error size. This is especially significant since vector errors cannot be avoided in natural image sequences.

[0043] The property of correcting vector errors will be illustrated based on the model of a horizontally moving ideal vertical edge between bright and dark pixels shown in FIG. 6, the faulty motion vector here having been estimated. FIG. 6 shows one line each of fields A_(n), B_(n) as well as intermediate image β_(n), and the respective erroneously estimated vectors.

[0044] In the example, it is assumed that the model edge is moving at a velocity of V_(xreal)=4 pixels/field, while the velocity has been erroneously estimated at v_(xest)=0 pixel/field with the result that the median masks in the previous and following images have been positioned at the same location, whereas the masks should have been correctly displaced relative to each other by 4 pixels.

[0045] In the example of FIG. 6, the median filter is supplied with image information from seven pixels of field A_(n) including from the pixel xo which marks the beginning of the dark region after the edge, which pixels lie within the selection mask, the mask under the image line here being shown by a bold outline. The median filter is also supplied with the image information from seven pixels of field B_(n) which lie outside the selection mask, outlined in bold above the image line, where the positions of these pixels correspond to the positions of the relevant pixels of field A_(n), since the selection mask was not shifted as a result of the incorrect estimation of motion. It is evident that the median filter has been supplied with image information or luminance information from eight dark pixels after the edge, and six bright pixels before the edge. The result of the median filtering of these pixels is a dark pixel, even giving uniformly weighted selection masks, which pixel is interpolated in the intermediate image at position x.

[0046] As a result of the median filter, the edge in intermediate image β_(n) that lies temporally in the center between fields A_(n), B_(n) is correctly interpolated to a position, despite the faulty motion vectors, which corresponds to half the distance between the edge in field A_(n) and the edge in field B_(n), as can be verified by the generation of luminance balances between the bright and dark pixels.

[0047] A linear interpolation filter, on the other hand, would produce a 4 pixel wide region of medium luminance, and thus cause a noticeable blurring of the edge [9].

[0048] In addition to the behavior of a correlated video signal, such as edges or areas, the behavior of a non-correlated video signal (such as irregular textures) may also be examined. It may be shown that weighted median filters are able, given proper selection of the filter weights, to obtain the details of the non-correlated image information when the correct estimation of motion is used. In the event a vector is erroneously estimated, fine details are extinguished—a result which according to reference [1] is preferred for 50 Hz to 100 Hz conversion over an erroneous position display.

[0049] In the method according to references [1, 2, 3], the masks shown in FIG. 8 are employed as the median masks in the lows channel. A star-shaped selection mask is used here for the pixels to be selected from fields A_(n) and B_(n), the starting points and end points of motion vector V_(AnBn) each being weighted by a factor 5 times greater than the surrounding values (factor 1). The median filter is additionally supplied with image information from a pixel of an intermediate image already determined.

[0050] A 3-tap median filtering takes place in the image regions uncovered in the lows channel [3].

[0051] For the highs channel, raster-dominant median filters are applied in the methods according to references [1, 2, 3], as was previously the case in the static method according to reference [4], the difference being that these are now vector-addressed. This method is shown in FIG. 8, which was taken from reference [3].

[0052] Reference [10] presents an IC implementation of an intermediate image interpolation method based on weighted median filters. An IC for format conversion based on a reliability-controlled median filter is described in [7].

[0053] Reference [8] presents an interpolation method that is also based on weighted median filters. However, here there is no separation of highs/lows, and no linear re-interpolation is performed if the vector addresses a line not present in the field. Instead, the median mask of FIG. 9a) is applied for the case in which the vector addresses an existing line—otherwise the median mask of FIG. 9b) is applied. If the sum total of the filter weights is an even number, the already calculated pixel of the intermediate image located to the left and above the actual position is incorporated into the median filter.

[0054] The interpolation method presented in reference [3] already achieves very good interpolation quality for conversion of 50 Hz interlaced signals to 100 Hz interlaced signals, this being attributable to the error correction properties of weighted median filters.

[0055] If one considers a format conversion in which the ratio of input image rate to output image rate deviates from the 50:100 ratio above, then other limiting conditions come into play. This becomes clear in FIG. 10 which shows the temporal positions of the input images for different output image rates when compared to a 50 Hz input sequence.

[0056] In the conversion to 100 Hz already considered, either the images of the output sequence temporally match an image of the input sequence, or the intermediate images to be interpolated are located in the display temporally precisely in between two input images. The second case produces the vector projection shown in FIG. 4 in which the measured or estimated motion vectors V_(AnBn), starting from a given pixel of the intermediate image, are projected with a projection factor of 0.5 into the previous or following original image, i.e., the image information of a pixel in the intermediate image is interpolated using the pixels located in those regions of the input images which lie at a distance calculated as a motion vector multiplied by 0.5 from the point to be interpolated.

[0057] In the case of other image rate ratios, however, intermediate images must be interpolated at temporal positions which are not located precisely in the center between two input images, with the result that projection factors other than 0.5 are produced for the intermediate images to be interpolated.

[0058] In addition, it is also evident that considerably more than one intermediate image to be temporally interpolated may lie between two output images which temporally precisely match one input image. Given a frequency of f_(org)=50 Hz for the input sequence, and an output frequency of f_(int)=60 Hz for the interpolated sequence, only every sixth output image, for example, temporally matches a given input image. Since the ratio of interpolated images to original images thus turns out to be significantly greater than for the conversion to f_(int) ₁₀₀ Hz, the quality requirements to be met by the interpolation method are similarly significantly higher as well.

[0059] In general, all possible projection factors produced by a conversion from forg to fint are determined by the equations:

pleft=k·f _(org) /f _(int) −|_k·f_(org) /f _(int—)|  (5)

and

pright=1−pleft=1−k·f _(org) /f _(int) +|_k·f_(org) /f _(int—)|  (6)

[0060] where k=0, 1, 2, . . . , k_(max)−1 and kmax=f_(int)/gcd (f_(org), f_(int)), and where |_k·f_(org)/f_(int—)| is the integer fraction of k·f_(org)/f_(int), and gcd denotes the operation for determining the greatest common divisor.

[0061] The projection factor pleft thus denotes temporal distance normalized for the period of the original signal between the intermediate image to be interpolated and the particular previous image/field, where the period represents the temporal distance between two successive images/fields of the original image sequence. In analogous fashion, pright denotes the temporal distance normalized for the period of the original signal between the intermediate image to be interpolated and the particular following image/field.

[0062] The two projection factors pright and pleft add up to 1.

[0063] It is evident that precisely kmax different interpolation phases or projection factor pairs, each with one left and right projection factors, are produced for an image rate conversion of f_(org) to f_(int). Subsequently, a cyclic repetition of the projection factors takes place.

[0064] The effect of vector errors for different projection factors varies in magnitude, as will be explained below where these effects of faulty motion vectors are examined in more detail for different projection phases.

[0065] For the sake of illustration, it is assumed that for a conversion of 50 Hz to 60 Hz the image ξ₁ of a 60-Hz sequence is to be interpolated from field A₃ and B₃ of the 50-Hz sequence as in FIG. 10. The associated projection factors pleft into the previous original image A₃ and pright into the following original image B₃ are determined to be pleft=⅙ and pright=⅚.

[0066] The following discussion is based, for the sake of illustration, on a progressive image display; however, the problems discussed are also found in analogous fashion in an interlaced display as well (=display in fields).

[0067] For the sake of illustration, let it be assumed that the estimated velocity is v_(est)=(12, 12), thereby producing on the previous image A₃ a projected velocity of

vpleft=pleft·v_(est)=⅙·(12, 12)=(2, 2)  (7)

[0068] and on the following image B₃ a projected velocity of

vpright=pright·v_(est)=⅚ (12, 12)=(10, 10)  (8)

[0069] with which the interpolation filter in the previous and following image A₃, B₃ are addressed or positioned starting from the pixel to be interpolated. The position of the pixel to be interpolated and the address position of the filter, which are shifted by vpright or vpleft starting from the point to be interpolated, are displayed in the intermediate image ξ in FIG. 11. The addressing position here denotes the position of the pixels in the original images onto which a filter mask for the selection of pixels to filter is placed. Given a correct estimation of the motion vector, the address positions correspond to the starting and end positions of a pixel moving from image A to image B.

[0070] If it is now assumed that the estimated motion vector has a maximum error of ±6 in the x axis and y axis, the error regions shown in gray in FIG. 11 are produced after vector projection, the positioning of the filter masks in original image B being much more strongly a function of vector errors than is the positioning of the filter in original image A.

[0071] In another example illustrating the effect of a vector error, again in regard to conversion from 50 Hz to 60 Hz, the image ξ₁ of FIG. 10 is interpolated from the original images A₃ and B₃, where the applicable terms for the projection factors are: pleft=⅙ and pright=⅚.

[0072]FIG. 12 illustrates the case in which an ideal vertical edge is moving horizontally at a velocity v_(real)=6 pixels/image. The estimated velocity, however, is v_(est)=0, with the result that filter masks W_(A) in image A and W_(B) in image G are positioned at the same place.

[0073] If the same filter masks are used for image A and image B according to the method outlined in reference [3], an interpolation of the moving edge in intermediate image ξ results at a position corresponding to half the distance between the edge in image A and in image B. This position is incorrect since the intermediate image to be interpolated does not lie temporally in the center between images A and B.

DESCRIPTION OF THE INVENTION

[0074] The goal of the invention is therefore to provide a method and a device for converting video signals that is capable of supplying a correct interpolation of intermediate images even in cases in which the ratio of the frequency of output image sequence to input image sequence deviates from 100:50, and particular does not have an integer value.

[0075] This goal is achieved by a method according to the features of claim 1 and a device according to the features of claim 16. Advantageous embodiments of the invention are the subject of the subdlaims.

[0076] The invention relates to a method for the motion-vector-based conversion of a first video signal which contains a first image sequence at a first frequency to a second video signal which contains a second image sequence at a second frequency, wherein at least some of the images of the second image sequence which are phase-shifted relative to the images of the first image sequence are generated by an interpolation of the images of the first image sequence such that, for a pixel of an image from the second image sequence, pixels at least from one first image and from one second image of the first image sequence are filtered by a median filter, wherein the median filter according to the invention is an adaptively weighted median filter.

[0077] In one embodiment, the median filter is dependent on an interpolation phase between the image to be interpolated of the second image sequence and the first and/or second image of the first image sequence, and therefore adaptively weighted as a function of the projection factors.

[0078] According to other embodiments, provision is made to adaptively weight the median filter as a function of local image information or of properties of a vector field of motion vectors.

[0079] In one embodiment implementing the method, for each of multiple different interpolation phases a first filter mask is generated which determines the selection and weighting of the pixels of the first image for median filtering, and a second filter mask is generated which determines the selection and weighting of the pixels of the second image for median filtering. These filter masks are selected and employed for filtering as a fimction of the instantaneous interpolation phase.

[0080] The following criteria are advantageously taken into account when generating the filter masks or filters:

[0081] The filter masks are selected so that a moving ideal horizontal or vertical edge is interpolated to the correct edge position for the specific interpolation phase regardless of an estimation error.

[0082] Given a correctly estimated motion vector, fine image details should also be preserved; this may be achieved by having the sum of the central weights of both filter masks be larger than the sum of the remaining weights.

[0083] The correctability of a weighted median filter generally increases with its size. For this reason, the extension of the filter masks preferably becomes larger as the associated projection factor becomes larger to allow the resulting larger erroneous regions to be corrected.

[0084] Since the information from the image with the smaller projection factor is as a rule more reliable, the majority of the filter weights are preferably assigned to the smaller projection factor, this effect becoming all the more pronounced the more the projection factors differ.

[0085] The probability that a mask will overlap multiple objects, and thus cause interpolation errors due to a violation of the model assumption (a moving edge), increases with increasing mask size. Detection of critical regions, for example edge detection, enables appropriate adaptation of the filter size. According to one embodiment, provision is made to provide more than one first filter mask and more than one second filter mask for each interpolation phase, which masks are correspondingly capable of filtering differently structured image regions, and to select the filter masks as a function of the detection of such image regions.

[0086] The reliability of the motion vectors is not uniform throughout the image. For example, due to the generally block-by-block estimation of motion, greater unreliability must be assumed in transitional regions between different vectors than within homogenous vector regions. In addition, the similarity measure by which the motion vectors are determined may be used for an assessment of reliability. Similarly, greater inaccuracies must be generally expected for greater vector quantities. For this reason, one embodiment adapts the size and weighting of the filter masks to the reliability of the motion vectors.

[0087] In addition, the velocity determined from the motion vector is also taken into account when generating the filter masks in order to be able to include a maximum correctable vector error for the motion vector in the design of the filter so that for an interpolation phase determined by the projection factor, multiple different filter masks may be archived or generated as a function of the velocity and the maximum correctable vector error.

[0088] Taking into account the above criteria, one-dimensional or two-dimensional weighted median filters may be developed using fairly well known, suitable filter design methods, which filters allow for a complete compensation of estimation errors within a specified error quantity with regard to the above requirements and to ideal horizontal or vertical edges.

[0089] Complete compensation of an estimation error here means that, for example, given an estimation error of ±6, the filter is able to correct the entire error region {−6, −6, . . . , −1, 0, 1, . . . , 5, 6}, taking into account the particular rounding concept to obtain pixel-precise filter addressing.

[0090] The invention is explained in more detail below using embodiments based on the figures.

[0091]FIG. 1 illustrates a method for generating pixels of an image line using a median filter according to the prior art.

[0092]FIG. 2 illustrates a method for generating a 100 Hz video signal from a 50 Hz video signal using median filtering for a lows channel according to the prior art.

[0093]FIG. 3 illustrates a method for generating a 100 Hz video signal from a 50 Hz video signal using median filtering for a highs channel according to the prior art.

[0094]FIG. 4 is a schematic illustration of a method for the motion-vector-based generation of an intermediate image from two fields of an original image sequence using an interpolation filter.

[0095]FIG. 5 is a schematic illustration of a method for generating a motion-vector-based intermediate image from two fields of an original image sequence using a weighted median filter as the interpolation filter.

[0096]FIG. 6 illustrates the motion-vector-based generation of an intermediate image from two fields of an original image sequence based on a selected line of the intermediate image.

[0097]FIG. 7 illustrates the interpolation method for the highs channel using state-of-the-art interpolation methods according to the publications [1, 2, 3].

[0098]FIG. 8 shows filter masks for state-of-the-art interpolation methods according to publications [1, 2, 3].

[0099]FIG. 9 shows interpolation masks for a state-of-the-art method described in reference [8].

[0100]FIG. 10 illustrates the temporal positions of images from interpolated image sequences as compared with a 50 Hz original sequence.

[0101]FIG. 11 illustrates the effect of an estimation error for different projection factors.

[0102]FIG. 12 shows one line each of a first and second image of an original sequence, and a line of an interpolated intermediate image, using non-adapted filters.

[0103]FIG. 13 shows one line each of a first and second image of an original sequence, and a line of an interpolated intermediate image, with an adaptively weighted median filter used according to the invention as in interpolation filter.

[0104]FIG. 14 shows a device according to the invention for generating a second video signal from a first video signal using an adaptively weighted median filter without band separation.

[0105]FIG. 15 shows a device according to the invention for generating a second video signal from a first video signal using an adaptively weighted median filter with band separation.

[0106]FIG. 16 illustrates an interpolation for the lows channel with band separation of the first video signal into a highs channel and a lows channel.

[0107]FIG. 17 illustrates a bilinear interpolation in the lows channel with band separation of the first video signal into a highs channel and a lows channel.

[0108]FIG. 18 illustrates a linear horizontal interpolation in the highs channel with band separation of the first video signal into a highs channel and a lows channel.

[0109]FIG. 19 illustrates an interpolation in the highs channel with band separation of the first video signal into a highs channel and lows channel.

[0110]FIG. 20 illustrates an interpolation according to the invention for a progressive input image sequence.

[0111] Unless otherwise indicated, the same reference numerals in the figures denote the same components with the same meaning.

[0112] With reference to the interpolation method illustrated in FIG. 6 and explained above, the following discussion explains the method according to the invention based on FIG. 13 in which the problems occurring with the prior art, which were explained based on FIG. 12, do not arise.

[0113]FIG. 13 shows one line each of successive images A and B of an intermediate image ξ generated by the method according to the invention. The example assumes that one edge moves between bright and dark pixels with a velocity of v_(xreal)=6 pixels/image, and a motion vector has not been correctly estimated at v_(est)=0 pixel/field.

[0114] Let the intermediate image ξ in the example be an intermediate image of the 60 Hz image sequence of FIG. 10, and let the images A and B be images of the 50 Hz image sequence in FIG. 10, where images A and B may be either fields or frames. In the example, the expression pleft=⅙ applies for the projection factor, while the corresponding expression pright=1−pleft=⅚ applies for the right projection factor.

[0115] A filter mask W_(A) is shown under the line of image A indicating which of the pixels of the line is given which weighting of a median filtering. A second filter mask W_(B) is shown above the line of image B indicating which of the pixels of the line is given which weighting of the median filtering. Both filters are positioned to select pixels by a starting point of the motion vector in image A, or by an end point of the motion vector in image B, although in the example these points coincide at point x₀ since the motion vector has been estimated to be zero. If the motion vector had been correctly estimated at v_(xreal), the filter mask W_(B) would be displaced to the right by six pixels.

[0116] In the filter masks shown, the central pixels determining the starting point and end point of the motion vector are more strongly weighted that the surrounding pixels, although the filter weights—in contrast to what is shown—may also decrease starting from the central pixel outwards with increasing distance from the central pixel. The pixel to be interpolated of the intermediate image lies vpleft=pleft·v_(est) pixels to the right relative to the starting point of the motion vector, and vpright=pright·v_(est) pixels to the left relative to the end point of the motion vector; its position here, given the incorrect estimation of the motion vector in the example, corresponds to the position of pixel x₀.

[0117] The projection factor pleft in FIG. 13 represents a measure of the normalized temporal distance between image A and intermediate image ξ, while the projection factor pright represents a measure of the normalized temporal distance between intermediate image ξ and image B, these projection factors being increasingly smaller as the temporal distance becomes smaller. The projection factors are thus a measure of the particular interpolation phase, i.e., the temporal position of intermediate image ξ relative to original images A, B, although during a conversion of f_(org) (e.g. 50 Hz) to f_(int) (e.g. 60 Hz) these interpolation phases change from intermediate image to intermediate image within a cycle determined by the ratio of these frequencies.

[0118] According to the invention, the filter masks are adaptively weighted as a function of the interpolation phases, i.e., as a function of the projection factors pleft, pright. The filter mask W_(A) here assigned to projection factor pleft has a smaller extension since the reliability of the image information of image A lying temporally closer to the intermediate image may be ranked as being higher than the reliability of image B lying temporally more distant from the intermediate image, and, as explained, the extension of the filter increases with decreasing reliability of the image information. For the projection phase shown with pleft=⅙ or pright=⅚, filter W_(A) has an extension of three pixels, while filter mask W_(B) has an extension of eleven pixels. The majority of the filter weights lie at 4+13+4=21 with filter W_(A) which weights the more reliable image information of image A, as compared with a total weight of 1+1+1+1+1+8+1+1+1+1+1=18 for filter mask W_(B).

[0119] The pixels of the intermediate line were generated by median-filtering the pixels of images A and B using masks W_(A) and W_(B); it is evident here that, thanks to the interpolation-phase-dependent weighting of filter masks W_(A), W_(B), the edge of the intermediate image has been correctly shifted by one pixel relative to the edge in image A, and by 5 pixels relative to the edge in image B, in accordance with the phase position of the intermediate image relative to images A and B. As a result, a motion-correct interpolation of the moving edge may be implemented in spite of the incorrectly estimated vector by using the adapted filter masks shown in FIG. 13. It is evident that the moving edge here has been interpolated with the correct position at V_(real)/6.

[0120] Information which has been introduced from an image with a small projection factor (picture A in FIG. 13) is significantly less dependent on the vector error, and may thus be ranked as more reliable information than that which has been introduced from an image with a larger projection factor (picture B in FIG. 13). In addition, the correlation of an image with a smaller projection factor is generally larger relative to the intermediate image to be interpolated since they lie temporally closer together. The application according to the invention of filter masks adapted to the actual interpolation phase, and thus to the specific projection factors—so-called polyphase interpolation filters—for the interpolation, exploits this fact to correctly interpolate intermediate images even in the event of incorrect estimation of motion.

[0121] According to the method according to the invention, provision is made to design a set of filters, of which at least one filter is assigned to one interpolation phase, and to switch between these filters as a function of the interpolation phase, or possibly other criteria. The term filter in the following discussion stands specifically for a group of two or more filter masks which determine the selection and weighting of pixels in the original images, and possibly of pixels already determined in the intermediate image to be interpolated, for one filter operation, i.e., for the interpolation of one pixel.

[0122]FIG. 14 shows an embodiment of a device for implementing the method according to the invention for the motion-vector-based conversion of a first video signal Sin which contains a first image sequence at a frequency f_(org) to a second video signal Sout which contains the second image sequence at a second frequency f_(int). The first video signal is supplied to an input of the device; the second video signal Sout is available at an output of the device.

[0123] The device has means 20 for motion estimation providing a motion vector which, in the example of FIG. 14, has an x-component v_(x) and a y-component v_(y). In order to estimate motion, at least two images of the input image sequence are supplied to motion estimation system 20, generally denoted in FIG. 14 as S(n) and S(n−1), where S(n) denotes a current image of the input image sequence and S(n−1) denotes a previous image of the input image sequence stored in an image buffer 50.

[0124] In addition, the device has a system 30 to provide a projection factor pleft dependent on the instantaneous interpolation phase, system 30 being designed to determine all k_(max) possible projection factors for a specified conversion of an input frequency f_(org) to an output frequency f_(int) according to equations (5) and (6), and to supply these in cyclical fashion such that the correct interpolation factor is available for each interpolation of an output image from two input images.

[0125] The device additionally has a filter mask selection system 40 which is supplied with at least the determined projection factor pleft and which is connected to an adaptive interpolator 10 which performs median filtering to provide one image each of the output image sequence from two images S(n), S(n−1) of the input image sequence. The filter masks to be used for the particular median filtering are selected by filter mask selection system 40 as a function of the projection factor pleft, then supplied to adaptive interpolator 10. In the filter mask selection unit, at least one group of two filter masks is stored for each interpolation phase, i.e., for each projection factor pleft, which determine the selection and weighting of the pixels to be included in filtering from image S(n), and the selection and weighting of the pixels to be included from image S(n−1). In addition, for each interpolation phase one filter mask may also be provided which determines the selection and weighting of already determined pixels of the intermediate image for median filtering in order to include these pixels as well in the filtering.

[0126] In the embodiment shown in FIG. 14, the selection of filter masks is performed additionally as a finction of the velocity vpleft and Vpright projected by equations (7) and (8) so as to be able to select filter masks that are capable of correcting an error of the estimated motion vector. Since the filter masks may have different extensions or different weightings as a function of the estimated velocity, this embodiment has multiple groups of filter masks available per interpolation phase, i.e., per projection factor pleft, in order to be able to correct a given maximum estimation error, where one group of filter masks is selected and supplied to the adaptive interpolator for each interpolation phase as a function of the projected velocities vp_(x) and vp_(y).

[0127] In addition, it is possible to provide different filters or filter masks for each interpolation phase, which masks differ, not only in their correction capability, but also in their estimated-error-correction property and/or detail preservation capability, for example, as a function of the local image information (for example, edges, periodic structures, etc.), value of the estimated motion vector, vector field homogeneity, or other image-related or motion-related criteria. In order for filter mask selection system 40 to select such filters, additional image evaluations are required which are performed in the system 80 shown by dashed lines in FIG. 14, in which one output signal which finctions in filter mask selection system 40 to select appropriate filter masks during an interpolation phase is supplied to this filter mask selection system 40. Filtering with adaptively weighted median filters by the method according to the invention may be applied either to the entire image or to individual subbands in connection with band separation, as shown for this device in FIG. 15.

[0128] This device has an adaptive highs interpolator 101 with an adaptively weighted median filter, and an adaptive lows interpolator 102 with an adaptively weighted median filter; wherein one filter mask selection system 410, 402 is assigned to each interpolator 101, 102; wherein projection factors pleft, projected velocities vp_(x) and vp_(y), as well as a so-called uncovering and covering flag are supplied to the filter mask selection systems; wherein this uncovering and covering flag, as well as the projected velocities vp_(x) and vp_(y), are generated in a system 50 for the purpose of vector projection or of uncovering and covering detection; wherein the motion vector v_(x), v_(y) and the projection factor pleft is supplied to this system 50.

[0129] The system has three image memories 501, 502, 503 connected in series so as to make available a series of four images/fields of the input image sequence S(n), S(n−1), S(n−2), S(n−3). These images/fields are sent to devices 601, 602, 603, 604 for band separation, wherein devices 601, 604 in this embodiment simply provide a highs signal which is supplied to the adaptive highs interpolator, while devices 602, 603 each provide a highs signal and a lows signal; the lows signals is supplied to the adaptive lows interpolator, and the highs signals to the adaptive highs interpolator.

[0130] The selection of filter masks by filter mask selection systems 401, 402 for interpolators 101, 102 is implemented as described in connection with FIG. 14, wherein for each individual interpolation phase the median filters or filter masks may be constant; however, multiple different median filters or filter masks may be provided for each individual interpolation phase which differ in the correction capability or in their detail preservation capability. The selected filter masks for the highs interpolator and the lows interpolator are, as a rule, different for each of the individual interpolation phases. The filter masks are selected as a function of the projection factors and/or as a function of the projected velocities vp_(x), vp_(y), and possibly in addition as a function of the uncovering and covering flag.

[0131] Vector projection as well as uncovering and covering detection implemented in system 50 are implemented as shown in reference [3], although here a generalization to random projection factors must be performed.

[0132] Various interpolations are performed for the highs channel and the lows channel, as will be explained below.

[0133] The interpolation of an intermediate image S(Z) for the lows channel from successive fields S(n−1), S(n−2) is illustrated in FIG. 16. The pixels included from original fields S(n−1), S(n−2) and the intermediate image are shown in bold outline, and the weighting factors (which are zero when a pixel is not to be considered) are sent to interpolators 101, 102 of filter mask selection units 401, 402. For example, for each interpolation phase, weighting factors for equal numbers of pixels are always sent to interpolators 101, 102, which pixels lie in the original fields S(n−1), S(n−2) in a rectangular arrangement or cruciform arrangement around the starting point and end point of the motion vector, and in the intermediate image S(Z) lie to the left or above the pixel about to be interpolated—individual filter masks differing, however, for different interpolation phases in their values for the weighting factors and in their positions for which the weighting factors are not equal to zero. It is possible, of course, to supply weighting factors only to the interpolators for pixels which are to be weighted with a weighting factor not equal to zero.

[0134] Starting with the interpolation pixel circled in FIG. 16, the position of which is also shown in bold in fields S(n−2), S(n−1), an addressing takes place, i.e., a positioning of the filter masks in the image S(n−2) preceding the intermediate image and in the following image.

[0135] Based on the filter weights W_((n−2))(i, j) dependent on the interpolation phase for the bolded pixels of the previous image S(n−1), and on the filter weights W_((n−1))(i, j) for the bolded pixels of the following image S(n−2), as well as on the filter weights for the bolded pixels W_(z)(i, j) of the intermediate image, which determine the recursive component, the starting value of the weighted median filter is determined by the expression: $\begin{matrix} {{S\left( {x,y,T_{z}} \right)} = {{Med}\begin{pmatrix} W_{\lbrack{N - 2}\rbrack} & {S\left( {{x - {p_{left} \cdot v_{x}}},{y - {p_{left} \cdot v_{y}}},T_{\lbrack{N - 2}\rbrack}} \right)} \\ W_{\lbrack{N - 1}\rbrack} & {S\left( {{x + {p_{right} \cdot v_{x}}},{y + {p_{right} \cdot v_{y}}},T_{\lbrack{N - 1}\rbrack}} \right)} \\ W_{z} & {S\left( {{x - 1},y,T_{z}} \right)} \end{pmatrix}}} & (9) \end{matrix}$

[0136] In the event the filter positions here starting from the interpolation pixel (x, y) projected into the previous image S(n−2) and the following image S(n−1) do not precisely coincide with a pixel position, the associated pixels are determined, as shown in FIG. 16, by a bilinear interpolation according to the expression:

S(x+dx, y+dy)=dx·dy·S(x+1, y+1)+(1−dx)·dy·S(x, y+1)+dx·(1−dy)·S(x+1, y)+(1−dx)·(1−dy)·S(x, y)  (10)

[0137] In order to generate a progressive starting image, all image lines (0, 1, 2, 3, . . . , 2R−1) are processed from intermediate image S(Z) according to FIG. 16 and Equation 15. If an interlaced starting image is to be generated, only the uneven (1, 3, . . . , 2R−1) or even (0, 2, . . . , 2R−2) image lines of the intermediate image S(Z) are processed, depending on the raster position of the intermediate image to be interpolated.

[0138] Interpolation in the highs channel is implemented in the manner explained below.

[0139] Starting with the interpolation pixel, an addressing of the filter masks similarly takes place in the highs channel in the image S(n−2) or S(n−1) preceding or following the intermediate image. To avoid any loss of resolution in the vertical axis, there is, however, no bilinear interpolation if the vectors do not precisely coincide with a pixel position. Instead, a different treatment is performed in the horizontal axis and vertical axis.

[0140] In the horizontal axis, a simple linear interpolation is implemented as in FIG. 18 according to the equation

S(x+dx, y)=dx·S(x+1, y)+(1−dx)·S(x, y)  (11)

[0141] In the vertical axis, the addressing positions of the filters are rounded up in the previous and following image to frame-line precision. If the rounded-up addressing position lies on a line which is not contained in the field, then the pixels lying above it or below it (if required, horizontally interpolated) are supplied to the median filter. If the rounded addressing position lies, however, on a line which is contained in the field, then the corresponding (if required, horizontally interpolated) pixel is introduced twice into the median filter.

[0142] These two possible cases are illustrated in FIG. 19. Only the value from the central filter weight W_(0,0) is considered in this example.

[0143] In the case shown in FIG. 19a in which the addressing of the central weight refers to point S(x, 2), and thus to a line contained in the field, the luminance value at position (x, 2) would be supplied to the filter twice.

S(x, y, T _(z))=Med( . . . , 2·W _([N−2]) (0, 0) S(x, 2, T _([N−2]), . . . )  (12)

[0144] In the case b) shown in FIG. 19b, on the other hand, the addressing of the central weight refers to point S (x, 1.5), and thus to a non-existent intermediate line. The expression supplied to the filter is thus

S(x, y, T _(z))=Med( . . . , W _([N−2]) (0, 0) S(x, 1, T _([N−2])), W _([N−2])(0, 0) S(x, 2, T _([N−2]), . . . )  (13)

[0145] The filter positions are treated analogously. In order to obtain an uneven number of filter weights, a non-motion-compensated additional value may be supplied from the image lying further back S(N−3) or from the following image S(N), as shown in FIG. 15. Of the two additional images, the image for which the pixels lie in the raster position to be interpolated is accessed here.

[0146] The interpolation method shown may, of course, be implemented without band separation as well. In this case, preferably the whole image is processed utilizing the signal processing methods for the lows channel explained above.

[0147] In the case of a progressive input image format, either a two-dimensional band separation may be performed, or a band separation is performed and the signal processing methods of the lows channel is utilized for the whole image.

[0148] Since there is no difference in processing for the vertical axis in the highs and lows channel, due to the lack of interlacing, the same interpolation scheme shown in the figure may be employed for both channels. The mask weights do differ, however, and are adapted to the perception properties in the particular channel. All band separations may be implemented either as linear or nonlinear band separations. In the case of a nonlinear band separation, the concepts “highs band/highs channel” and “lows band/lows channel” are to be understood analogously since there is then no separation in the spatial frequency band.

[0149] The method according to the invention is based on the use of vector-based weighted median filters in which, in contrast to previous methods, adaptive switching of interpolation filters is provided as a function of the particular interpolation phase and/or of the particular local image information or local vector field properties. As a result, the method may also be adapted to different image rate conditions. The benefit of the interpolation method according to the invention compared to existing interpolation methods is especially great when the number of interpolated images is high relative to the number of images which may be taken over directly or raster-re-interpolated from the input sequence. This is the case, for example, for conversion from 50 Hz to 60 Hz, and for implementation of synthetic slow motion.

APPENDIX Prior Art

[0150] [1] Blume, H.: “Nichtlineare fehlertolerante Interpolation von Zwischenbildem” [“Nonlinear Error-Tolerant Interpolation of Intermediate Images”] Dissertation, Dortmund University, VDI-Fortschrittberichte, VDI Verlag, 1997

[0151] [2] Blume, H: “A New Algorithm for Nonlinear Vector-based Upconversion with Center Weighted Medians,” SPIE Journal ofElectronic Imaging, Vol. 6, No. 3, pp. 368-378, 1997

[0152] [3] Blume, H.; Jostschulte, K.: “Verfahren und Schaltungsanordnung zur Flimmerreduktion für ein Gerät zur Videosignalverarbeitung” [“Method and Circuitry for Reducing Flicker in a Device for Video Signal Processing”] DE 1905758 A1, 1996

[0153] [4] DE 44 14 173 of Sep. 28, 1994 entitled: “Verfahren und Schaltungsanordnung zur Flimmerreduktion für ein Gerät zur Videosignalverarbeitung” [“Method and Circuitry for Reducing Flicker in a Device for Video Signal Processing”]

[0154] [5] Botteck, M.: “Mehrdimensionale Interpolationstechniken für eine verbesserte Wiedergabe von Videosignalen” [“Multidimensional Interpolation Techniques for Improved Reproduction of Video Signals”] Dissertation, Dortmund University, Shaker Verlag, 1993

[0155] [6] de Haan, G.; Biezen, P.; Ojo, O.: “An Evolutionary Architecture for Motion Compensated 100 Hz Television,” IEEE Transactions on Circuits and Systems for Video Technology, Vol. 5, No. 3, pp. 207-217, June 1995.

[0156] [7] Hahn, M.; Ruck, C.; Rüler, K.; Talmi, M.; Wolf, S.: “Single-Chip Videokonverter ‘Hicon’ für Multimedia-Displays” [“‘Hicon’ Single-Chip Video Converter for Multimedia Displays”], ITG Fachtagung, Eighth Dortmund Video Seminar, pp. 217-222, 1999

[0157] [8] Ohm, J.-R.; Braun, M.; Rümler, K.; Blume, H.; Schu, M.; Tuschen, C.: “Low-Cost System für universelle Video-Formatkonversion” [“Low-Cost System for Universal Video Format Conversion”], ITG Fachtagung, Seventh Dortmund Video Seminar, pp. 251-256, 1997

[0158] [9] Schröder, H.; Blume, H.: “Mehrdimensionale Signalverarbeitung, Band 2:

[0159] Architekturen und Anwendungen für Bilder und Bildsequenzen” [Multidimensional Signal Processing, Vol. 2: Architectures and Applications for Images and Image Sequences”], Teubner Verlag, Stuttgart, 2000

[0160] [10] Schu, M.; Scheffler, G.; Tuschen, C.; Stolze, A.: “System on Silicon-IC for Motion Compensated Scan Rate Conversion, Picture-in-Picture Processing, Split Screen Applications and Display,” IEEE Transactions on Consumer Electronics, Vol. 45, No. 3, pp. 842-850, 1999.

[0161] [11] Helmut Schönfelder: Digitale Filter in der Videotechnik [Digital Filters in Video Technology], Drei-R-verlag, Berlin, 1988, pp. 125-127.

[0162] [12] Rosenfeld, Kak: Digital Picture Processing. Academic Press, Inc., 2^(nd) Edition, 1982, pp. 261-263. 

1. Method for motion-vector-based conversion of a first video signal which contains a first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ) at a first frequency (f_(org)) to a second video signal which contains a second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ) at a second frequency (f_(int)), wherein at least some images from the second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ) which are phase-shifted relative to images from the first image sequence (A₁, B₁, A₂, B_(2,) A₃, B₃, . . . ) are generated by an interpolation of the images from the first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ) such that for a pixel of an image (ξ₁) from the second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ), pixels at least from one first image (A₃) and one second image (B3) from the first image sequence (A₁, B₃, A₂, B₂, A₃, B₃, . . . ) are filtered by a median filter, characterized in that the median filter is an adaptively weighted median filter.
 2. Method according to claim 1, wherein the median filter is adaptively weighted as a function of an interpolation phase between the image to be interpolated (ξ₁) from the second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ) and the first and/or second image (A₃, B₃) from the first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ).
 3. Method according to claim 1, wherein the median filter is adaptively weighted as a function of the interpolation phase between the image to be interpolated (ξ₁) from the second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ) and the first and/or second image (A₃, B₃) from the first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ), and as a function of the motion vector.
 4. Method according to claims 1, 2, or 3, wherein the median filter is adaptively weighted as a function of local image information.
 5. Method according to one of the foregoing claims, wherein the median filter is adaptively weighted as a function of local properties of the motion vector (V_(AnBn)).
 6. Method according to one of the foregoing claims, wherein for each of multiple different interpolation phases a first filter mask (W_(A)) is generated which determines the selection and weighting of the pixels of the first image (A₃), and wherein for each interpolation phase a second filter mask (W_(B)) is generated which determines the selection and weighting of the pixels of the second image (A₃), and wherein the generated filter masks are selected as a function of the instantaneous interpolation phase for median filtering.
 7. Method according to claim 6, wherein the filter masks (W_(A), W_(B)) are designed such that central pixels selected by the filter masks are more strongly weighted than surrounding pixels, wherein the sum of the central weights is greater than the sum of the remaining weights.
 8. Method according to claims 6 or 7, wherein the filter masks (W_(A), W_(B)) are selected such that the larger the number of particular pixels processed by the filter masks from the images (A₃, B₃) of the first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ) the smaller is the particular projection factor (pleft, pright), wherein the particular projection factor is the temporal distance, normalized for the temporal distance between the first and second image (A₃, B₃), between the interpolated image (ξ₁) and the first image (A₃), or between the interpolated image (ξ₁) and the second image (B3).
 9. Method according to one of claims 6 through 8, wherein the filter masks (W_(A), W_(B)) are selected such that the majority of the filter weights lie with that one of the two filter masks which is assigned to the smallest projection factor (pleft, pright).
 10. Method according to one of claims 6 through 9, wherein for each interpolation phase, more than one first filter mask and more than one second filter mask is provided that are capable of filtering variously structured image regions, and wherein the filter masks are selected as a function of a detection of such image regions.
 11. Method according to one of claims 6 through 10 wherein for each interpolation phase, more than one first filter mask and more than one second filter mask are provided that are capable of filtering in connection with motion vectors of varying reliability, wherein the filter masks are selected as a function of an estimated reliability of the motion vector.
 12. Method according to one of the foregoing claims, wherein already generated pixels of the interpolated image (ξ₁) from the second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ) are also supplied to the median filter.
 13. Method according to one of the foregoing claims, wherein the first video signal is separated into at least two subband signals by linear or nonlinear band separation, and the method is implemented for each of the subband signals.
 14. Method according to one of the foregoing claims, wherein the first video signal is an interlaced signal.
 15. Method according to one of claims 1 through 13, wherein the first video signal is a progressive signal.
 16. Application of a method according to one of the foregoing claims to generate a synthetic slow-motion signal.
 17. Device for the motion-vector-based conversion of a first video signal (Sin) which contains a first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ) at a first frequency (f_(org)) to a second video signal (Sout) which contains a second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ) at a second frequency (f_(int)), wherein the device has the following features: a means for motion estimation (20; 201, 202) which provides a motion vector (v_(x), v_(y)), an interpolation system (10; 101, 102) with a median filter which is designed to generate images of the second image sequence (α₁, β₁, γ₁, δ₁, ε₁,ξ₁, . . . ) which are phase-shifted relative to images of the first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ) such that for a pixel of an image (ξ₁) from the second image sequence (α₁, β₁, γ₁, δ₁, ε₁, ξ₁, . . . ), pixels at least from one first image (A₃) and one second image (B₃) of the first image sequence (A₁, B₁, A₂, B₂, A₃, B₃, . . . ) are filtered, a means (30; 301, 302) for providing a projection factor (pleft, pright), wherein the particular projection factor is the temporal distance, normalized for the temporal distance between the first and second image (A₃, B₃), between the image (ξ₁) and the first image (A₃), or between the image (ξ₁) and the second image (B₃), a filter mask selection unit (40; 401, 402) coupled to an interpolation system, to which at least the projection factor (pleft) is supplied as the input signal, and which makes available filter masks dependent on the input signals to the interpolation system (10; 101, 102).
 18. Device according to claim 17, which has a vector projection unit, to which a projection factor (pleft) and a motion vector (v_(x), v_(y)) are supplied, and which provides a projected motion vector (vp_(x), vp_(y)) which is supplied to a filter mask selection unit (40; 401, 402).
 19. Device according to claim 17 or 18, which has the following features: a means for band separation (60; 601, 602) which provides at least one first band signal and at least one second band signal from the first video signal (Sin), a first interpolation system (101) connected to a first filter mask selection unit (401), and a second interpolation system (102) connected to a second filter mask selection unit (402), wherein the at least one first band signal is supplied to the first interpolation system (101), and the at least one second band signal is supplied to the second interpolation system (102), a logic circuit (70), to which an output signal of the first interpolation system (101) and an output signal of the second interpolation system (102) are supplied, and which provides the second video signal (Sout) from the output signal of the first interpolation system (101) and from the output signal of the second interpolation system (102). 